Using a truly accessible and reader-friendly approach, this comprehensive introduction to statistics redefines the way statistics can be taught and learned. Unlike other books that merely focus on procedures, Reid’s approach balances development of critical thinking skills with application of those skills to contemporary statistical analysis. He goes beyond simply presenting techniques by focusing on the key concepts readers need to master in order to ensure their long-term success. Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics. Keeping computational challenges to a minimum, Reid shows readers not only how to conduct a variety of commonly used statistical procedures, but also when each procedure should be utilized and how they are related. Following a review of descriptive statistics, he begins his discussion of inferential statistics with a two-chapter examination of the Chi Square test to introduce students to hypothesis testing, the importance of determining effect size, and the need for post hoc tests. When more complex procedures related to interval/ratio data are covered, students already have a solid understanding of the foundational concepts involved. Exploring challenging topics in an engaging and easy-to-follow manner, Reid builds concepts logically and supports learning through robust pedagogical tools, the use of SPSS, numerous examples, historical quotations, insightful questions, and helpful progress checks.

Finding Differences With Interval And Ratio Data—III : The One-Way Between-Subjects ANOVA

Finding Differences With Interval And Ratio Data—III : The One-Way Between-Subjects ANOVA

Enter to grow in wisdom.

—Inscription on the outside of the 1890 gate to Harvard Yard

As Table 11.1 indicates, when testing for a difference with interval or ratio data, a commonly used procedure is the analysis of variance (abbreviated ANOVA). It is best to think of ANOVA as a general approach to analyzing data rather than as a specific procedure, for ANOVAs can be used with a wide variety of experimental designs.

Recall that in an experiment, the researcher is in control or manipulates something, such as how much sleep subjects get. The researcher then looks at the consequence. Thus, two things vary in a simple experiment. One variable is controlled or manipulated by ...

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